The present invention relates to methods according to the preamble parts of claims 1, 2 and 5 and to an equalizer for performing such methods.
This invention relates to equalizing received digital data by a maximum likelihood sequence estimator (MLSE) and more specifically to obtaining branch metrics for a maximum likelihood sequence estimator.
The MLSE using the Viterbi-Algorithm (VA) bases its symbol decisions on probabilistic decision variables, i.e. branch and path metrics differences, that are ultimately related to conditional probabilities of observing a given received signal when a given symbol (or symbol sequence) has been sent.
The basis for computing the relevant sequence decision variables are the so-called branch metrics, which in turn are based on a probabilistic model of the channel defined by a set of amplitude probability density functions (PDFs) or probability mass functions (PMFs), one for each channel state, i.e. for each sent bit pattern of a certain length.
In essence, for the detection to best approximate a true maximum likelihood detector, the metrics should represent the log-likelihoods for the events to observe specific quantized amplitudes when given symbol sequences have been sent, i.e. when the channel was in given channel states.
In a practical system, the probabilistic channel model needs to be estimated in real-time and without channel-specific a-priori information. Moreover it needs to be updated in real-time in order to follow changing channel conditions e.g. due to drifts or due to dynamic effects such as polarization mode dispersion (PMD). This implies that the channel estimator needs to be blind and adaptive.
To learn or acquire the channel model at the beginning of operation, the channel estimator is initialized with a crude channel model, resulting in a high initial error rate.
Channel model estimation methods may be parametric or nonparametric (cf. H. F. Haunstein, W. Sauer-Greff, A. Dittrich, K. Sticht, and R. Urbansky, “Principles for electronic equalization of polarization-mode dispersion” J. Lightwave Technol., vol. 22, pp. 1169-1182, April 2004, and cf. Langenbach, S.; Bosco, G.; Poggiolini, P.; and Kupfer, T., “Parametric versus Non-Parametric Branch Metrics for MLSE-based Receivers with ADC and Clock Recovery,” Optical Fiber communication/National Fiber Optic Engineers Conference, 2008. OFC/NFOEC 2008, Conference on, Paper JThA60, 2008). When a parameterized functional form of the PDF is assumed, a parametric method estimates the PDF parameters and uses the functional form to compute the metrics. On the other hand, non-parametric methods do not assume knowledge of the PDF (cf S. Langenbach and N. Stojanovic, “Channel estimation and sequence estimation for the reception of optical signal”, EP1 494 413 A1, Jan. 5, 2005 (later referred to as COEP4); A. Farbert, S. Langenbach, N. Stojanovic, C. Dorschky, T. Kupfer, C. Schulien, J.-P. Elbers, H. Wernz, H. Griesser, C. Glingener, “Performance of a 10.7 Gb/s receiver with digital equaliser using maximum likelihood sequence estimation,” in Proc. ECOC, Stockholm, 2004, Th.4.1.5; J.-P. Elbers, H. Wernz, H. Griesser, C. Glingener, A. Faerbert, S. Langenbach, N. Stojanovic, C. Dorschky, T. Kupfer, C. Schulien, “Measurement of the dispersion tolerance of optical duobinary with an MLSE-receiver at 10.7 Gb/s,” in Proc. OFC, Washington, 2005, OthJ4). COEP4 is zo incorporated herein by reference and cites further references.
FIG. 8 shows an optical receiver 10 which is essentially known from COEP4 and receives an analog input signal r(t) from an optical fiber 4. The receiver 10 comprises a physical interface (PI) 11, a AGC or variable gain amplifier (VGA) 12, an ADC 13, a clock recovery (CR) subsystem 14, a sampling phase adjustment (SPA) circuit 15, an MLSE 17, a FEC decoder 18, a channel model unit 19 and a receiver control node 9.
The physical interface 11 performs an optical-to-electrical (O/E) conversion. The physical interface (PI) uses either a pin diode or an avalanche photo diode to convert the incident optical power to an electrical current. A transimpedance amplifier (TIA) is used to amplify and convert the photo-current to a voltage.
The analog serial signal data at the output of physical interface 11 is amplified by a high-gain high-dynamic, low-noise automatic gain control (AGC) or variable gain amplifier (VGA) circuit 12. The output signal of AGC 12 is designated {tilde over (r)}(t):
The ADC 13 digitizes the analog signal {tilde over (r)}(t) and outputs quantized data yt,s. Index t refers to a time slot and index s refers to different sampling phases. Index s may assume the values 1 to S for S-fold oversampling. S may be be 2. The ADC 13 receives a sampling clock from SPA circuit 15 which in turn receives a sampling clock from clock recovery subsystem 14. The SPA circuit 15 operates as an adjustable delay in order to optimize the phase of the clock which is to say to optimize the sampling times of ADC 13.
The quantized data yt,s are input into MLSE 17. MLSE 17 may implement a Viterbi algorithm (VA) and outputs the most likely sequence designated detected data ut to FEC decoder 18. In a typical optical receiver, with a powerful FEC code used, the bit error rate at the output of MLSE 17 ranges e.g. from 10−2 to about 10−4. The subsequent FEC decoder 18 further reduces bit error rate to a range between 10−9 and 10−16 which is required for data transmission. FEC decoder 18 outputs decoded data xi for further processing. MLSE 17 and/or FEC 18 may obtain BER estimates and provide same to control node 9. Actually, the serial data output by the ADC are, in reality, de-multiplexed in the digital domain. Blocks 17, 18, 19, 9 all operate at lower speed.
Control node 9 receives a loss-of-signal (LOS) signal from physical interface 11 and may receive counter values or event frequency information from channel model unit 19 in order to obtain pre-processed statistics data for controlling the AGCNGA circuit 12, CR 14 and SPA circuit 15. Counter values may also be referred to as bin values. Important for this invention is that the channel model unit 19 receives quantized data yt,s. The channel model unit 19 further receives the present channel state bt and calculates and outputs branch metrics to the MLSE 17.
Returning to channel model estimation methods, we focus our interest on the non-parametric method, which uses empirical histograms being synonymous to empirical PMFs to obtain the branch metrics. This is generally called the histogram method (cf. O. E. Agazzi, M. R. Hueda, H. S. Carrer, and D. E. Crivelli, “Maximum-likelihood sequence estimation in dispersive optical channels”, “J. Lightwave Technol., vol. 23, pp. 749-763, February 2005). More specifically, when the measured histogram bin values representing relative frequencies are directly converted to metrics values, without further post-processing, we call it the canonical histogram method, as described in more detail in the following.